Complete autopsies of 9 deceased COVID-19 patients (5 males and 4 females) with 15 median hospitalization days (IQR, 10-22) before death were performed (Table 1). The median of ages was 67 years old (IQR, 63-78). Except for the missing comorbidity records of 2 cases (cases 6 and 9), the other 7 cases had comorbidities. Specifically, 7/7 patients had hypertension, 2/7 (cases 1 and 4) had cerebral infarction, 2/7 (cases 5 and 8) had coronary artery disease, and 1/7 (case 7) had gout. Of note, one case (case 5) had hypertension, coronary artery disease, renal dysfunction, lacunar infarction, and chronic bronchitis with emphysema. A total of 8/9 patients died mainly due to respiratory failure with multiple organ failure, and the other 1/9 died due to sudden cardiac death from acute coronary heart disease.
In addition to diffuse alveolar damage in the lung, the predominant histological findings included hyaline thrombi among all 9 deceased patients (Figure 1). Specifically, 9/9 cases exhibited microthrombi in the hilar arteriole, alveolar wall capillary and interstitial vascular lumen of the lung; 4/9 (1, 2, 3, and 5) in the subarachnoid arteriole and parenchymal small endovascular lumen of the brain; 4/9 (1, 2, 3, and 5) in the small vascular lumen of the spleen; 2/9 (cases 2 and 9) within the kidney; and 1/9 (case 4) in the coronary artery lumen together with hemorrhage. To evaluate the coagulation state before death, we also extracted the last hematological indices relevant to hemostasis, i.e., platelet, prothrombin time, activated partial thromboplastin time, thrombin time, fibrinogen, and D-dimer in these cases when they were alive (Table 1). The ISTH DIC scores in 8/9 cases matched the grade of DIC (³5 points).
The autopsy results of thrombi in the major organs of the body and their DIC before death strongly indicated coagulation abnormalities in COVID-19 patients (Table 1, Figure 1). Together with previous reports demonstrating the high relevance of blood glucose, total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein with coagulation,31-34 we then included those indices in our clinical data analyses. The clinical time-sequential data included 407 hospitalized patients with confirmed COVID-19. Their demographic and clinical characteristics are shown in Table 2. The median (IQR) age was 62.0 years (51.0-58.0) with an overall range from 6 years to 92 years; 51.8% of the patients were from 40 to 65 years of age. A total of 49.9% were female. Of all the patients, 184 (45.2%) had at least 1 of the following 3 comorbidities: coronary artery disease (40 [9.8%]), hypertension (144 [35.4%]), and diabetes (72 [17.7%]). Since all the patients included in this study had either been discharged from the hospital with SARS-CoV-2 negativity (368 [90.4%]) or died (39 [9.58%]), the median duration of hospitalization was 15 days (8.0-25.0).
Among all 407 patients with different symptoms throughout the hospital stay (the other 208 patients were excluded since they were still in the hospital on March 26), 253 patients (62.2%) exhibited mild infection, 73 patients (17.9%) showed severe infection, and 81 patients (19.9%) showed critical infection (Figure S1). All relevant clinical records were reviewed to classify the patients into critical, severe, or mild groups according to the Chinese National Health Commission (NHC) guidelines (7th trial edition) for COVID-19 pneumonia.26 The patients with critical or severe infection were significantly older than those with mild infection (median [IQR] age, 65.0 [57.0-72.0] or 67.0 [60.0-73.0] years vs 59.2 [45.0-59.0] years; both P values < 0.0001) and more likely to stay longer at hospital after admission (median 19.0 days [7.0-35.0] or 21.0 [14.0-26.0] vs 13.0 [8.0-22.0]; both P values < 0.001), whereas there was no difference between severe and critical cases (P = 0.5996 for age and P = 0.2806 for hospital stay). Moreover, compared to mildly infected patients, patients with severe infection were more likely to have other underlying comorbidities (47 [64.4%] vs 98 [24.1%]; P = 0.0001), especially hypertension (36 [49.3%] vs 77 [30.4%]; P = 0.0028) (Table 2, Table S1).
However, 81 critical patients exhibited slightly similar underlying comorbidities as mild patients (39 [48.1%] in critical patients, P = 0.1339), and no significant difference in hypertension proportion was noted between these patients (31 [38.3%] in critical patients, P = 0.1894) (Table S1). Furthermore, despite no statistically significant difference (P = 0.2806), the median hospitalization days of critical patients were slightly reduced compared with those of severe patients (19.0 [7.0-35.0] vs. 21.0 [14.0-26.0]). This led us to think about whether the nonsurvivors displayed any differences in the critical group.
Table S2 demonstrates that when compared with the survivors (42 (51.9%) patients in the critical group) in the critical group, fewer nonsurvivors had underlying comorbidities (13 [33.3%] vs 26 [61.9%]; P =0.01), such as hypertension (8 [20.5%] vs 23 [54.8%]; P =0.002) and diabetes mellitus (3 [7.7%] vs 12 [28.6%]; P = 0.02). Thirty-nine nonsurvivors also had shorter hospital stays after admission compared with 42 survivors (median 10.0 days [6.5-16.5] vs 35.0 [21.3-40.5]; P < 0.0001). In addition, the percentages of comorbidities and hospital duration in survivors were more similar to those in the severe group compared with those in nonsurvivors from the critical group (Table 2).
To determine the major hematological features that appeared during COVID-19 thrombogenic progression, the temporal changes in 11 clinical laboratory indices, including platelet (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen (FIB), D-dimer, blood glucose (GLU), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL), were tracked on admission until outcome (Table S3). All 407 patients with definite discharge status were analyzed and displayed using a line chart (Figure 2). During hospitalization, most patients had increased D-dimer levels, and those with critical infection continued to exhibit significantly higher D-dimer levels after admission until the outcome. Intriguingly, PLT in the critical patients exhibited a marked decrease on admission, and the counts remained low until day 14 (both P < 0.05 compared to mild and severe patients [Table S3]) and then gradually increased. In addition, the indices, e.g., PT and GLU, in critical patients exhibited persistent prolonged time, higher score or higher level during the hospitalization compared with those in severe or mild patients, whereas other indices, e.g., TC and HDL, in the critical patients were lower at the initial stage and remained at a relatively low level until the outcome. On the other hand, indices, such as LDL, exhibited changes at the late stage, and TT was intermittently prolonged after admission in the critical patients. In contrast, APTT, FIB, and TG exhibited no discernable difference among patients with different levels of severity during hospitalization. We also found persistently high DIC scores in critical patients during hospitalization using the ISTH DIC scoring system.
To evaluate the severity of the coagulation state in patients with different disease levels, we also evaluated the percentages of the peak values of 11 indices in every individual who exhibited a value beyond the normal range during hospitalization. Table S4 and Table S5 summarize the median (IQR) of the level of all the maximum values of 11 indices in every individual patient during hospitalization, the proportions of out-of-normal-range values of all the patients with different levels of severity and their statistical test results. Consistent with previous findings4,35, the median concentrations of D-dimer (μg/mL) in all types were greater than the normal range (all patients, 1.15 [0.39-3.18]; normal range, 0-0.5). Nonetheless, critical patients exhibited significantly higher D-dimer levels than those with the other two types (critical median [IQR] vs severe median [IQR] or mild median [IQR]; 16.77 [3.21-12.9] vs 1.60 [0.78-2.94] or 0.53 [0.28-1.39]; two P both < 0.0001). Other coagulation parameters, i.e., PT (17.9 s [15.6-21.9] vs 14.3 s [13.9-14.8] or 14.0 s [13.4-14.5]; two P both < 0.0001), APTT (52.9 s [44.1-68.7] vs 43.6 s [39.9-47.9] or 40.5 s [37.8-44.0]; two P both < 0.0001) and TT (20.0 s [17.3-27.0] vs 17.4 s [16.8-17.7] or vs 16.8 [16.1-17.8]; two P both < 0.0001) were also prolonged in this peak value evaluation. Consistently, the out-of-normal-range portions of those coagulation parameters in critical patients were significantly greater than those in severe or mild patients, e.g., D-dimer (79/80 [98.8%] vs 64/72 [88.9%], P =0.0101; or vs 127/247 [51.4%], P < 0.0001), which is similar to that noted in the DIC grades.
Our autopsy results and 81 critical patients strongly suggested that differences existed between survivors and nonsurvivors, so progression analyses of laboratory hematological indices were also performed to evaluate the severity of coagulation. Since all patients were confirmed to be either discharged with SARS-CoV-2 negativity or deceased, we defined the date of discharge or death as day 1 before the outcome, and the previous dates increased backward (Figure 3, Table S6). Interestingly, most indices of survivors and nonsurvivors exhibited similar trends, medians, and portions of abnormal values for all critical patients. However, when dividing the critical patients into survivors and nonsurvivors, several indices exhibited significant differences between these groups. Combining analysis with the maximum values of individual patients, nonsurvivors presented with fewer platelets (×109/L)(216.0 [142.5-273.5] vs 287.5 [199.0-370.0]; P = 0.0035), prolonged prothrombin time (20.2 s [18.1-25.3] vs 16.7 s [15.3-18.0]; P < 0.0001), elevated D-dimer levels (21.00 μg/mL [13.42-21.00] vs 6.79 μg/mL [2.82-20.61]; P = 0.0013) and greater DIC score (6 [5-7] vs 5 [5-6]; P = 0.0002) compared with survivors (Table S7, Table S8) with striking differences noted close to the destination date (P = 0.0027 and P = 0.0051 for PLT and PT at day 11 before outcome, respectively; P = 0.0063 and P = 0.0193 for D-D at day 12 before outcome (Figure S6). Notably, although no obvious changes were noted when dividing all patients into 3 groups (Figure 2), the subgroup of nonsurvivors manifested a significantly higher level of fibrinogen compared with that of survivors at days 7 and 9 before the outcome (Figure 3, Table S6).
To further explore the underlying correlation between these groups, a heat map was applied to visualize the Spearman correlation coefficient between each clinical feature or laboratory index (Figure 4). “Severity” in the heatmap indicated the severities of COVID-19, i.e., mild, severe, and critical classifications. As indicated by the heat map, the features that positively correlated with patient classifications included coagulation indices, e.g., PT (Spearman correlation 0.46), APTT (Spearman correlation 0.31), and D-dimer (Spearman correlation 0.46), and others indices, e.g., GLU (Spearman correlation 0.42). However, other indices, including TC (Spearman correlation -0.42), HDL (Spearman correlation -0.54), and LDL (Spearman correlation -0.54), exhibited a significantly negative correlation with patient classifications. We further applied those data to the normal distribution curve to estimate the relationship of those features with severity (Figure 4). Unlike the age-severity distribution with the critical group’s mean between the severe group and mild group (Figure 4), coagulation index-severity distributions, including PT, APTT, and D-dimer, were positively associated with the mild-severe-critical distribution. Other indices, such as TC, HDL, and LDL, were negatively associated (Figure S2). To explore which indices played an indispensable role, a random forest model was constructed according to patient classifications. The best accuracy of the model is 83.8%, the maximum depth of the tree is 9, and the number of classifiers is 50 (Figure S2). The model revealed the importance of each feature (Figure 4, Figure S2). The most important feature was PT followed by D-dimer. These two features contributed 40% importance to the total. The red dotted line together with the black dotted line separated the features that totaled 90% importance. Taken together, these data suggest the important role of coagulation and hematological indices during the progression of COVID-19.